Predictive population balance modeling of pharmaceutical tablet disintegration and dissolution behavior.

Area of Science:

• Pharmaceutical Sciences
• Chemical Engineering
• Computational Modeling

Background:

• Predicting tablet disintegration and dissolution is complex due to nonlinear interactions between material attributes and physicochemical properties.
• Current methods often require extensive experimental data, hindering efficient formulation development.

Purpose of the Study:

• To introduce a population balance model (PBM) framework for simulating tablet disintegration and dissolution.
• To reduce the experimental dataset required for predicting tablet performance.
• To capture the influence of critical material attributes (CMAs) and manufacturing routes on tablet behavior.

Main Methods:

• Developed an integrated PBM framework to model granule and crystal evolution during tablet disintegration and dissolution.
• Incorporated breakage kernels accounting for tablet porosity, granule properties, and swelling-driven diffusion.
• Utilized a modified Noyes-Whitney equation for dissolution modeling and experimentally determined size distributions as inputs.

Main Results:

• The PBM framework accurately predicted the dissolution profiles of acetaminophen, indomethacin, and lomustine tablets.
• Achieved similarity factor f2 values above 50, indicating good agreement between predicted and experimental dissolution data.
• Demonstrated the framework's adaptability for both granulation and direct compression manufacturing routes.

Conclusions:

• The semi-mechanistic PBM framework offers mechanistic insights into tablet dissolution behavior.
• This modeling approach significantly reduces the need for extensive experimental dissolution testing.
• The framework has the potential to accelerate and facilitate tablet formulation development.